




"""simple docstring"""







from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin

@dataclass
class 				snake_case_			(					_UpperCAmelCase			):







   """simple docstring"""




   A_				      =       42

class 				snake_case_			(					_UpperCAmelCase	, _UpperCAmelCase			):







   """simple docstring"""




   @register_to_config
   def __init__(		self       ,					lowerCamelCase_ = 3_2       ,					lowerCamelCase_ = 6_4       ,					lowerCamelCase_ = 2_0       ,					lowerCamelCase_ = 7_6_8       ,					lowerCamelCase_=7_7       ,					lowerCamelCase_=4       ,					lowerCamelCase_ = 0.0       ,					lowerCamelCase_ = "silu"       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = "linear"       ,					lowerCamelCase_ = "prd"       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = None       ,					)				->   Tuple:
       super().__init__()
       UpperCamelCase              =	num_attention_heads
       UpperCamelCase              =	attention_head_dim
       UpperCamelCase              =	num_attention_heads * attention_head_dim
       UpperCamelCase              =	additional_embeddings

       UpperCamelCase              =	time_embed_dim or inner_dim
       UpperCamelCase              =	embedding_proj_dim or embedding_dim
       UpperCamelCase              =	clip_embed_dim or embedding_dim

       UpperCamelCase              =	Timesteps(lowerCamelCase_       ,					lowerCamelCase_       ,					0)
       UpperCamelCase              =	TimestepEmbedding(lowerCamelCase_       ,					lowerCamelCase_       ,					out_dim=lowerCamelCase_       ,					act_fn=lowerCamelCase_)

       UpperCamelCase              =	nn.Linear(lowerCamelCase_       ,					lowerCamelCase_)

       if embedding_proj_norm_type is None:
           UpperCamelCase              =	None
       elif embedding_proj_norm_type == "layer":
           UpperCamelCase              =	nn.LayerNorm(lowerCamelCase_)
       else:
           raise ValueError(F'unsupported embedding_proj_norm_type: {embedding_proj_norm_type}')

       UpperCamelCase              =	nn.Linear(lowerCamelCase_       ,					lowerCamelCase_)

       if encoder_hid_proj_type is None:
           UpperCamelCase              =	None
       elif encoder_hid_proj_type == "linear":
           UpperCamelCase              =	nn.Linear(lowerCamelCase_       ,					lowerCamelCase_)
       else:
           raise ValueError(F'unsupported encoder_hid_proj_type: {encoder_hid_proj_type}')

       UpperCamelCase              =	nn.Parameter(torch.zeros(1       ,					num_embeddings + additional_embeddings       ,					lowerCamelCase_))

       if added_emb_type == "prd":
           UpperCamelCase              =	nn.Parameter(torch.zeros(1       ,					1       ,					lowerCamelCase_))
       elif added_emb_type is None:
           UpperCamelCase              =	None
       else:
           raise ValueError(
               F'`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.')

       UpperCamelCase              =	nn.ModuleList(
           [
               BasicTransformerBlock(
                   lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					dropout=lowerCamelCase_       ,					activation_fn='''gelu'''       ,					attention_bias=lowerCamelCase_       ,					)
               for d in range(lowerCamelCase_)
           ])

       if norm_in_type == "layer":
           UpperCamelCase              =	nn.LayerNorm(lowerCamelCase_)
       elif norm_in_type is None:
           UpperCamelCase              =	None
       else:
           raise ValueError(F'Unsupported norm_in_type: {norm_in_type}.')

       UpperCamelCase              =	nn.LayerNorm(lowerCamelCase_)

       UpperCamelCase              =	nn.Linear(lowerCamelCase_       ,					lowerCamelCase_)

       UpperCamelCase              =	torch.full(
           [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]       ,					-1_0_0_0_0.0)
       causal_attention_mask.triu_(1)
       UpperCamelCase              =	causal_attention_mask[None, ...]
       self.register_buffer('''causal_attention_mask'''       ,					lowerCamelCase_       ,					persistent=lowerCamelCase_)

       UpperCamelCase              =	nn.Parameter(torch.zeros(1       ,					lowerCamelCase_))
       UpperCamelCase              =	nn.Parameter(torch.zeros(1       ,					lowerCamelCase_))







   @property
   # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
   def   UpperCAmelCase__			(		self)				->   Dict[str, AttentionProcessor]:
       UpperCamelCase              =	{}

       def fn_recursive_add_processors(lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_):
           if hasattr(lowerCamelCase_       ,					'''set_processor'''):
               UpperCamelCase              =	module.processor

           for sub_name, child in module.named_children():
               fn_recursive_add_processors(F'{name}.{sub_name}'       ,					lowerCamelCase_       ,					lowerCamelCase_)

           return processors

       for name, module in self.named_children():
           fn_recursive_add_processors(lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_)

       return processors







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_)				->   Tuple:
       UpperCamelCase              =	len(self.attn_processors.keys())

       if isinstance(lowerCamelCase_       ,					lowerCamelCase_) and len(lowerCamelCase_) != count:
           raise ValueError(
               F'A dict of processors was passed, but the number of processors {len(lowerCamelCase_)} does not match the'
               F' number of attention layers: {count}. Please make sure to pass {count} processor classes.')

       def fn_recursive_attn_processor(lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_):
           if hasattr(lowerCamelCase_       ,					'''set_processor'''):
               if not isinstance(lowerCamelCase_       ,					lowerCamelCase_):
                   module.set_processor(lowerCamelCase_)
               else:
                   module.set_processor(processor.pop(F'{name}.processor'))

           for sub_name, child in module.named_children():
               fn_recursive_attn_processor(F'{name}.{sub_name}'       ,					lowerCamelCase_       ,					lowerCamelCase_)

       for name, module in self.named_children():
           fn_recursive_attn_processor(lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   List[str]:
       self.set_attn_processor(AttnProcessor())







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = True       ,					)				->   Dict:
       UpperCamelCase              =	hidden_states.shape[0]

       UpperCamelCase              =	timestep
       if not torch.is_tensor(lowerCamelCase_):
           UpperCamelCase              =	torch.tensor([timesteps]       ,					dtype=torch.long       ,					device=hidden_states.device)
       elif torch.is_tensor(lowerCamelCase_) and len(timesteps.shape) == 0:
           UpperCamelCase              =	timesteps[None].to(hidden_states.device)

       # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
       UpperCamelCase              =	timesteps * torch.ones(lowerCamelCase_       ,					dtype=timesteps.dtype       ,					device=timesteps.device)

       UpperCamelCase              =	self.time_proj(lowerCamelCase_)

       # timesteps does not contain any weights and will always return f32 tensors
       # but time_embedding might be fp16, so we need to cast here.
       UpperCamelCase              =	timesteps_projected.to(dtype=self.dtype)
       UpperCamelCase              =	self.time_embedding(lowerCamelCase_)

       if self.embedding_proj_norm is not None:
           UpperCamelCase              =	self.embedding_proj_norm(lowerCamelCase_)

       UpperCamelCase              =	self.embedding_proj(lowerCamelCase_)
       if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
           UpperCamelCase              =	self.encoder_hidden_states_proj(lowerCamelCase_)
       elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
           raise ValueError('''`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set''')

       UpperCamelCase              =	self.proj_in(lowerCamelCase_)

       UpperCamelCase              =	self.positional_embedding.to(hidden_states.dtype)

       UpperCamelCase              =	[]
       UpperCamelCase              =	0

       if encoder_hidden_states is not None:
           additional_embeds.append(lowerCamelCase_)
           additional_embeddings_len += encoder_hidden_states.shape[1]

       if len(proj_embeddings.shape) == 2:
           UpperCamelCase              =	proj_embeddings[:, None, :]

       if len(hidden_states.shape) == 2:
           UpperCamelCase              =	hidden_states[:, None, :]

       UpperCamelCase              =	additional_embeds + [
           proj_embeddings,
           time_embeddings[:, None, :],
           hidden_states,
       ]

       if self.prd_embedding is not None:
           UpperCamelCase              =	self.prd_embedding.to(hidden_states.dtype).expand(lowerCamelCase_       ,					-1       ,					-1)
           additional_embeds.append(lowerCamelCase_)

       UpperCamelCase              =	torch.cat(
           lowerCamelCase_       ,					dim=1       ,					)

       # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
       UpperCamelCase              =	additional_embeddings_len + proj_embeddings.shape[1] + 1
       if positional_embeddings.shape[1] < hidden_states.shape[1]:
           UpperCamelCase              =	F.pad(
               lowerCamelCase_       ,					(
                   0,
                   0,
                   additional_embeddings_len,
                   self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
               )       ,					value=0.0       ,					)

       UpperCamelCase              =	hidden_states + positional_embeddings

       if attention_mask is not None:
           UpperCamelCase              =	(1 - attention_mask.to(hidden_states.dtype)) * -1_0_0_0_0.0
           UpperCamelCase              =	F.pad(lowerCamelCase_       ,					(0, self.additional_embeddings)       ,					value=0.0)
           UpperCamelCase              =	(attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype)
           UpperCamelCase              =	attention_mask.repeat_interleave(self.config.num_attention_heads       ,					dim=0)

       if self.norm_in is not None:
           UpperCamelCase              =	self.norm_in(lowerCamelCase_)

       for block in self.transformer_blocks:
           UpperCamelCase              =	block(lowerCamelCase_       ,					attention_mask=lowerCamelCase_)

       UpperCamelCase              =	self.norm_out(lowerCamelCase_)

       if self.prd_embedding is not None:
           UpperCamelCase              =	hidden_states[:, -1]
       else:
           UpperCamelCase              =	hidden_states[:, additional_embeddings_len:]

       UpperCamelCase              =	self.proj_to_clip_embeddings(lowerCamelCase_)

       if not return_dict:
           return (predicted_image_embedding,)

       return PriorTransformerOutput(predicted_image_embedding=lowerCamelCase_)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_)				->   str:
       UpperCamelCase              =	(prior_latents * self.clip_std) + self.clip_mean
       return prior_latents






"""simple docstring"""





import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		int = 8     ):







      '''simple docstring'''




      lowerCAmelCase    		=			ascii_letters + digits + punctuation
      return "".join(secrets.choice(SCREAMING_SNAKE_CASE     ) for _ in range(SCREAMING_SNAKE_CASE     )     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		int     ):







      '''simple docstring'''




      i -= len(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			i // 3
      lowerCAmelCase    		=			i % 3
      # chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
      #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
      lowerCAmelCase    		=			(
          chars_incl
          + random(SCREAMING_SNAKE_CASE				,		quotient + remainder     )
          + random(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
          + random(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
      )
      lowerCAmelCase    		=			list(SCREAMING_SNAKE_CASE     )
      shuffle(SCREAMING_SNAKE_CASE     )
      return "".join(SCREAMING_SNAKE_CASE     )

      # random is a generalised function for letters, characters and numbers


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		int     ):







      '''simple docstring'''




      return "".join(secrets.choice(SCREAMING_SNAKE_CASE     ) for _ in range(SCREAMING_SNAKE_CASE     )     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		List[Any]				,		SCREAMING_SNAKE_CASE	:		List[Any]     ):







      '''simple docstring'''




      pass  # Put your code here...


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		List[Any]				,		SCREAMING_SNAKE_CASE	:		int     ):







      '''simple docstring'''




      pass  # Put your code here...


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[Any]				,		SCREAMING_SNAKE_CASE	:		Dict     ):







      '''simple docstring'''




      pass  # Put your code here...


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		int = 8     ):







      '''simple docstring'''




      if len(SCREAMING_SNAKE_CASE     ) < min_length:
            # Your Password must be at least 8 characters long
            return False

      lowerCAmelCase    		=			any(char in ascii_uppercase for char in password     )
      lowerCAmelCase    		=			any(char in ascii_lowercase for char in password     )
      lowerCAmelCase    		=			any(char in digits for char in password     )
      lowerCAmelCase    		=			any(char in punctuation for char in password     )

      return upper and lower and num and spec_char
      # Passwords should contain UPPERCASE, lowerase
      # numbers, and special characters


def      UpperCAmelCase__				(    ):







      '''simple docstring'''




      lowerCAmelCase    		=			int(input("""Please indicate the max length of your password: """     ).strip()     )
      lowerCAmelCase    		=			input(
          """Please indicate the characters that must be in your password: """     ).strip()
      print("""Password generated:"""				,		password_generator(SCREAMING_SNAKE_CASE     )     )
      print(
          """Alternative Password generated:"""				,		alternative_password_generator(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )				,		)
      print("""[If you are thinking of using this passsword, You better save it.]"""     )


if __name__ == "__main__":
       main()






